Beep Inc., provider of autonomous shared mobility solutions, has officially announced the launch of Beep AutonomOS, which is a platform designed to let public transit operators and mobility-as-a-service companies integrate autonomous mobility services rapidly and seamlessly into their solutions. You see, software-driven mobility gets dubbed as the next big thing in the logistics industry because of the way it leverages autonomous mobility networks to combine real-time service optimization with greater efficiency and performance. But what makes AutonomOS an ideal candidate to fulfill this growing market? Well, the answer resides in its ability to provide safe, scalable, cost-effective multi-passenger autonomous mobility services. Making the whole solution more important is a fact that it provides comprehensive services capability for the deployment and management of autonomous passenger services either as a standalone solution or with integration into multimodal operations. Talk about the whole value proposition on a more granular level, the solution in question comes decked up with a unified view of service performance, fleet health. and on-road operations. Then, we have dedicated government tools to ensure mission compliance and passenger safety. Once the safety and legality concerns are duly addressed, AutonomOS brings to the fore service optimization features that integrate service performance, smart city infrastructure, and ridership data to drive greater service efficiency, optimize passenger experience, and maximize ridership across the system. Moving on, the product also has on offer service definition and planning functions to support a variety of service modes from fixed route to demand-responsive variants, while simultaneously boasting a machine learning-powered in-cabin monitoring functionality. The latter element is there to facilitate an immediate response in the event of a passenger safety or roadway issue. Another thing which enhances the prospects of this solution talks to its support for leading ADS providers, a sense of support that is further backed up by data protocol and a toolkit to enable rapid integration with further platforms. In case the whole offering still doesn’t sound attractive enough to you, then it must be mentioned how AutonomOS is even made to be outright compatible in the context of wider data standards including GTFS (General Transit Feed Specification), and GTFS-RT (General Transit Feed Specification Realtime).

“Autonomous vehicles are capable of safely navigating our streets from waypoint to waypoint, but lack the concepts of mission, service and passenger,” said Joe Moye, CEO of Beep. “Beep AutonomOS fills a void in the autonomy landscape by introducing management and orchestration logic enabling the integration of autonomous vehicles into public mobility networks. More importantly, AutonomOS adds an additional layer of functionality to address passenger safety and comfort concerns in advance of fully unattended autonomous deployments.”

Founded in 2019, Beep’s rise to prominence stems from an ability to plan, deploy, and manage autonomous shuttles in dynamic mobility networks. By doing so, the company connects people and places with solutions that reduce congestion, eliminate carbon emissions, improve road safety and enable mobility for all.

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The researching teams at Massachusetts Institute of Technology and MIT-IBM Watson Lab have successfully developed a technique which can empower deep-learning models to adapt to new sensor data, and more importantly, do so directly on an edge device. Before we unpack the whole development, we must try and gain an idea about the problem statement here. Basically, deep-learning models that enable artificial intelligence chatbots, in order to deliver the customization expected from them, need constant fine-tuning with fresh data. Now, given how smartphones and other edge devices lack the memory and computational power required for such a fine-tuning process, the current framework tries to navigate through that by uploading user data on cloud servers where the model is updated. So, what’s the problem here? Well, the problem talks to the data transmission process exhausting huge amounts of energy. Not just energy, there is also security risks involved, as you are sending  sensitive user data to a cloud server which always carry a risk of getting compromised. Having covered the problem, we should now get into how exactly the new technique takes on it. Named PockEngine, the new solution comes decked up with the means to determine what parts of a huge machine-learning model need alterations to improve accuracy. Complimenting the same is a fact that it only stores and computes with those specific pieces, thus leaving the rest undisturbed and safe. This marks a major shift, because up until now, whenever we would run an AI model, it instigated inference, a process where data input is passed from layer to layer till the time a prediction is generated. Hold on, the main issue presents itself after the said process is done. You see, during training and fine-tuning, the model undergoes a phase known as backpropagation. Backpropagation, in case weren’t aware, involves comparing the output to the correct answer. Next up, it runs the model in reverse, and each layer is updated as the model’s output gets closer to the correct answer. With each layer required to be duly updated, the entire model and intermediate results have to be unquestionably stored, making the fine-tuning mechanism pretty high maintenance. There is, fortunately enough, a loophole which suggests that not all layers in the neural network are important for improving accuracy, and even for layers that are important, the entire layer may not need to be updated. Hence, the surplus components don’t need to be stored. Furthermore, you also don’t have to revisit the very first layer to improve accuracy because the process can be stopped somewhere in the middle. Understanding these loopholes, PockEngine first fine-tunes each layer, one at a time, on a certain task, and then measures the accuracy improvement after each individual layer. Such a methodology can go a long way when it comes to identifying the contribution of each layer, as well as trade-offs between accuracy and fine-tuning cost, while automatically determining the percentage of each layer that needs to be fine-tuned.

“On-device fine-tuning can enable better privacy, lower costs, customization ability, and also lifelong learning, but it is not easy. Everything has to happen with a limited number of resources. We want to be able to run not only inference but also training on an edge device. With PockEngine, now we can,” said Song Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, a distinguished scientist at NVIDIA, and senior author of an open-access paper describing PockEngine.

Another manner in which the solution sets itself apart is concerned with the timing aspect. To put it simply, the traditional backpropagation graph is generated during runtime, meaning it demands a massive load of computation. On the other hand, PockEngine does the same during compile time, just as the model is being prepared for deployment. It essentially deletes bits of code to remove unnecessary layers or pieces of layers and create a pared-down graph of the model to be used during runtime. Then, the solution performs other optimizations on this graph to further improve efficiency. Turning this feature all the more important is a fact that the entire process needs to be conducted only once.

The researchers have already performed some initial tests on their latest brainchild. The stated tests saw them applying PockEngine to deep-learning models on different edge devices, including Apple M1 Chips, and the digital signal processors common in many smartphones and Raspberry Pi computers. Going by the available details, the solution performed on-device training up to 15 times faster, and that it did without witnessing any drop in accuracy. Apart from that, it also made a big cut back on the amount of memory required for fine-tuning. Once this bit was done, they then moved on to applying the solution across large language model Llama-V2. Here, the observations revealed that PockEngine was able to reduce the each iteration’s timeframe from seven seconds to less than one second.

“This work addresses growing efficiency challenges posed by the adoption of large AI models such as LLMs across diverse applications in many different industries. It not only holds promise for edge applications that incorporate larger models, but also for lowering the cost of maintaining and updating large AI models in the cloud,” said Ehry MacRostie, a senior manager in Amazon’s Artificial General Intelligence division.

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In the midst of a global shift towards sustainable energy solutions, Electric Vehicles have emerged as the poster child for eco-conscious transportation. With governments, corporations, and individuals increasingly embracing EVs, the spotlight has turned to EV Charging Management, a crucial aspect of this electrifying revolution and sustainability. In this article, we embark on a journey through the intricacies of EV Charging Management, uncovering its significance, challenges, and the promising future it promises to bring.

The EV Revolution: A Charge Towards a Greener Tomorrow

Electric vehicles have taken center stage in the fight against climate change. The transition from conventional ICE vehicles to EVs offers a ray of hope for reducing CO2 and curbing air pollution. As EV adoption surges worldwide, ensuring a robust and efficient charging infrastructure becomes imperative.

The EV Charging Ecosystem: A Closer Look

At the heart of the EV revolution lies the EV charging ecosystem, a complex network of charging stations, energy providers, EV manufacturers, and software solutions. To understand the nuances of EV Charging Management, we must dissect this ecosystem and explore its components.

1. Charging Stations

EVCS are the lifelines of the EV charging ecosystem. These stations come in various forms, from public charging stations dotting urban landscapes to residential chargers nestled in our homes. The availability, accessibility, and convenience of these stations play a pivotal role in shaping the EV experience.

2. Energy Providers

Powering EVs requires electricity, and energy providers are the key players in this game. They ensure that the electrons needed to charge EVs are clean, reliable, and readily available, but this is also not so easy to achieve due to las of resources, manpower, etc.

3. EV Manufacturers

Automakers are racing to develop cutting-edge EVs with longer ranges and faster charging capabilities. These innovations challenge charging management systems to keep pace with evolving technologies.

4. Software Solutions

EV Charging Management relies heavily on software solutions. These systems coordinate charging sessions, optimize energy usage, and provide real-time data to both users and operators. They are also the most important key to sustainability and to ensure no downtime and seamless use of the chargers.

The Challenges of EV Charging Management

While the potential of EVs is undeniable, the path to widespread adoption is fraught with challenges. Let’s explore some of the roadblocks on the journey to seamless EV Charging Management.

Fig.1: EV Management System

1. Range Anxiety

One of the most prominent concerns among potential EV owners is range anxiety. The fear of running out of battery power before reaching a charging station is a major hurdle, demanding enhanced charging infrastructure and faster charging technology.

2. Infrastructure Gaps

Charging station availability varies widely, with rural areas often lacking the necessary infrastructure. Bridging these gaps is crucial to ensure fair EV adoption.

3. Charging Speed

Charging speed is crucial, especially for long trips. Deploying fast chargers while considering grid capacity is a logistical challenge that demands careful planning.

4. Billing and Payment

The complexity of billing and payment systems can frustrate EV users. Simplifying the process and ensuring interoperability between different charging networks is vital for a hassle-free experience.

The Art of EV Charging Management

In the face of these challenges, EV Charging Management emerges as the hero of the story. This intricate dance of technology, infrastructure, and user experience holds the key to unlocking the full potential of EVs.

1. Charging Scheduling and Optimization

Effective Charging Management begins with scheduling. Software solutions allow users to plan charging sessions during off-peak hours, reducing strain on the grid and minimizing energy costs. AI-driven algorithms predict energy demand and adjust charging times accordingly.

2. Grid Integration

Smart grid technology plays a vital role in EV Charging Management. It enables seamless integration between EV charging and the electricity grid, allowing for load balancing and grid stability. Bidirectional charging, where EVs can discharge energy back into the grid during peak demand, is an exciting development.

3. User-Friendly Apps

User-friendly mobile apps make the EV charging experience a breeze. They help locate charging stations, provide real-time availability information, and offer convenient payment options. Moreover, these apps can tailor charging profiles to individual user preferences.

4. Data-Driven Insights

Data is king in the world of EV Charging Management. Collecting and analyzing data from charging sessions helps optimize infrastructure deployment, identify usage patterns, and plan for future expansion.

The Road Ahead: A Sustainable Tomorrow

As we navigate the twists and turns of EV Charging Management, a promising future awaits. Governments, businesses, and innovators are joining forces to build a charging ecosystem that leaves no one behind.

1. Investment in Infrastructure

Governments worldwide are investing heavily in EV charging infrastructure. Initiatives such as the installation of public charging stations, tax incentives, and rebates for EV owners, and grid modernization efforts are paving the way for an electrified future.

 Table 1: Government Initiatives for EV Charging Infrastructure

2. Interoperability

Efforts are underway to create a global standard for EV charging, ensuring that users can access any charging network seamlessly. This interoperability is akin to the universal acceptance of credit cards, making it easier for EV owners to charge wherever they go.

3. Innovation in Battery Technology

Breakthroughs in battery technology promise longer ranges and faster charging times. Solid-state batteries, for instance, could revolutionize the EV landscape, making charging even more convenient.

Figure 1: Evolution of EV Battery Technology

4. The Rise of Electric Fleets

Corporate fleets are electrifying rapidly. The adoption of electric vehicles in delivery services, ridesharing companies, and public transportation not only reduces emissions but also drives the demand for efficient charging solutions.

Conclusion

EV Charging Management is the linchpin of the electric vehicle revolution. With its ability to address challenges, optimize charging, and create a seamless user experience, it is steering us towards a sustainable future. As governments, businesses, and individuals come together to invest, innovate, and embrace change, the road to a greener tomorrow is well-lit and ready to be explored. In the grand narrative of our time, EV Charging Management is the plot twist that promises to electrify the world.

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Workplace EV charging stations can help those employees stay focused on the workday rather than scanning apps to see where they can get a top up before their next task, event, responsibility, obligation. For employers, it can make the difference between attracting and retaining top talent. There are benefits on both sides of this equation.

Let’s say you see those benefits, but the idea of designing and implementing an EV charging program seems daunting.

Why Start a Workplace EV Charging Program

Among workplace challenges, the struggle to find, hire, and retain talent is one of the most talked about and most prevalent. And, as more workers return to the office, often as part of a hybrid work from home program, employers are trying to make the workplace seem more attractive.

One way to attract talent and ease the back to office transition is to add EV charging stations. In fact, nearly 60% of EV drivers have said that workplace EV charging is vital for them and a determining factor in employment. Further, in one study 58% of respondents reported that a company’s sustainability efforts impacted their choice regarding where to work.

There are additional options that allow business owners to leverage EV charging as part of a rewards and recognition program or even a workplace benefit. However, there are also opportunities for a revenue stream, depending on how you want to manage your workplace EV charging program.

For employees, the benefits of workplace EV charging are pretty clear. Most EV drivers charge at home because that’s where they have time to get a full charge and, with home chargers (Level 1) they need that kind of time. For most Americans, we spend nearly as much time at work as we do at home. For workers with a hybrid schedule, that still means a few days a week with a car parked for 4-8 hours at the workplace. With a Level 2 charger, that’s a perfect amount of time to fully charge or just get a top up.

And, if employees have after work errands, or need to get kids to and from activities, or even just a decent commute, charging at work means they can do any or all of those things without range anxiety. In turn, you have happier, less stressed employees, who can effectively manage their work life balance by getting where they need to be after work.

Is Your Workplace Ready for EV Charging?

The simple answer is yes, we’re all ready, but the real answer is a bit more complicated. In addition to some preliminary site analysis, you’ll want to ask some of the same questions we ask prior to implementation of potential public EV charging locations:

• Is there demand?
• Do you have employees who currently drive electric vehicles? Employees who plan to purchase an EV in the next year?
• Are there other charging stations in the area? (this may impact how you implement rather than whether you install or not)
• Do you have your own parking lot or parking garage?
• Is there access from an electrical panel to the potential location of EV charging stations?
• Would your business benefit from the presence of EV charging stations and a focus on sustainability?
• Would EV charging stations help you meet sustainability goals?
• Are there company vehicles which could be transitioned to electric vehicles in the future?

This is a starting point. Obviously, when it comes time for implementation and installation, there’s more work to be done, but if you’ve answered yes to most of those questions, you’re ready to get started!

Challenges to Workplace EV Charging

As the above questions hint, your parking situation could possibly be a challenge. That’s why working with a turnkey solution provider, one who will walk you through the process from site analysis to installation, is important. You’ll want the team architecting your site to have an in-depth understanding of every phase of the project.

For example, a small lot is a challenge, but not one that is insurmountable. It may limit the location and number of EV charging stations, but a small lot suggests a smaller company, and this may be sufficient.

Another challenge, that we often see across sites, is power management. The power available to your site via the grid may be limited. The nature of your business may mean that during certain hours of the day there’s a particularly big draw on that available power. You may be sharing power with other buildings. In this way, power management becomes a primary focus for EV charging station owners.

With the right EV charging platform management system (CPMS), however, you can manage those power loads and adjust charging capacity to ensure you don’t overburden the grid. Further, depending on your site, your business, and a few other factors, there may be government grants of tax incentives available to you to help upgrade your site.

The final challenge workplaces may face are the same struggles others face when there is greater demand than available charging stations. More specifically, cars stay parked at a single charging station even though the vehicle is fully charged, or multiple vehicles and drivers want to charge at the same time. Thankfully, how you implement your EV charging program can help you mitigate this challenge.

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Autodesk has officially announced the launch of Autodesk AI, which is designed to unlock creativity, solve problems, and eliminate non-productive work across the industries that design and make the world around us. Available across the wider Autodesk portfolio, the stated solution comes well-equipped with an ability to deliver at your disposal intelligent assistance and generative capabilities that allow customers to imagine and explore freely, while simultaneously producing precise, accurate, and innovative results. Talk about how this will happen on a more granular level, the answer is rooted in a collection of dedicated sub-solutions present within the product. For instance, when we bring up architecture, engineering, and construction industry, Autodesk AI will begin its value proposition from Autodesk Forma. This one can effectively provide rapid wind, noise, and operational energy analysis so to help you conduct smart early-stage planning and take design decisions that improve outcomes rather meaningfully. Next up, we have the prospect of InfoGraphic, a Machine Learning Deluge Tool bearing the responsibility to offer feedback on the best placement for retention ponds and swales. Such functionality should help users in preventing or reducing the impact of water disasters. Moving on, then there is AutoCAD, which leverages artificial intelligence to help drafters iterate faster through handwritten notes and digital markups. The idea around AutoCAD is to determine the intent of the user to recommend context-aware actions for easily incorporating changes. The last bit of prominent details across this space comes from Construction IQ, a tool meant to again use AI to predict, prevent, and manage construction risks that might impact quality, safety, cost, or schedule.

The discipline we will now get into is of product design and manufacturing, where Autodesk will use its Blank AI acquisition to enable conceptual design exploration for the automotive industry. By doing so, it will birth accelerated outcomes, alongside 3D models that can be rapidly created, explored, and edited in real time using semantic controls and natural language, and guess what, you won’t need any advanced technical skills whatsoever around here. Another way through which Autodesk AI will enhance design and manufacturing space is through Autodesk Fusion, which allows customers to automatically generate product designs that are optimized for manufacturing method, performance, cost, and more. Furthermore, Fusion workflows are being specifically conceived to ensure automated creation of templatized Computer-Aided Manufacturing toolpaths that can be adjusted by the user as needed. Complimenting the same are automated drawings that will provide interactive experiences in sheet creation, view placement, and annotation workflows.

“As the trusted technology partner for Design and Make industries, Autodesk sees AI as a way for our customers to tackle the challenges they face and turn them into opportunities,” said Andrew Anagnost, President and Chief Executive Officer at Autodesk. “AI is the future of design and make, and Autodesk is pioneering this transition. We sit at the junction of many of the most creative and impactful industries in the world. We’ll continue to invest in AI because of its transformational potential to drive better outcomes for our customers’ businesses and the world.”

Rounding up the highlights is what Autodesk AI has on the offer for media and entertainment industry. For starters, the product banks upon generative scheduling capabilities in Autodesk Flow to automate scheduling for media and entertainment productions, doing so on an actionable note by managing the constantly shifting variables between teams and budgets. Notably, this generative scheduling approach produces results in minutes for a process which has traditionally taken days at a time. Given the dramatic difference, teams can predict, plan, and right-size resources to ensure creative bandwidth wherever needed. Turning our attention to Autodesk Flame, this one knows a thing or two about automating manual tasks such as keying, sky replacement, beauty work, and camera tracking for artists. These people can also expect to interact with the company’s 3D animation software called Maya and access its scene data using natural language text prompts. To make the offering all the more significant, Autodesk has also collaborated with Wonder Dynamics, a collaboration where AI will power a Maya plug-in to automatically animate, light, and compose computer-generated characters for live-action scenes.

The entire development provides an interesting follow-up to one recent State of Design and Make special report on AI, which claimed that out of all the companies surveyed, 77% revealed that they are planning to increase or strongly increase investment in AI over the course of next three years. As for all the leaders who were surveyed, 66% agree that in two to three years AI will be essential. But what makes Autodesk an ideal candidate to make the most of this raging trend? Well, apart from all the AI-driven solutions, the company’s credentials are also markedly rooted in the fact that it has, till date, published more than 60 peer-reviewed research papers advancing the state of the art in AI and generative AI.

“With a commitment to security and ethical AI practices, we’re focused on delivering responsible AI solutions that address our customers’ needs,” said Raji Arasu, Chief Technology Officer at Autodesk. “Autodesk AI will continue to surface across the Autodesk platform–both in our existing products and our industry clouds–to enable better ways of designing and making.”

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The researching teams at Massachusetts Institute of Technology and Computer Science and Artificial Intelligence Laboratory (CSAIL) have successfully developed a system called Feature Fields for Robotic Manipulation (F3RM), which is designed to blend 2D images with foundation model features and create 3D scenes that help robots identify and grasp nearby items. According to certain reports, F3RM comes decked up with an ability to interpret open-ended language prompts from humans, an ability making it extremely useful around real-world environments that contain thousands of objects like warehouses and households. But how does the system work on a more granular level? Well, the proceedings begin from F3RM taking picture on a given selfie stick. Mounted on the stated stick, the camera snaps 50 images at different poses, enabling it to build a neural radiance field (NeRF), a deep learning method which takes 2D images to construct a 3D scene. The resulting RGB photos collage creates a “digital twin” of its surroundings in what is a 360-degree representation that shows what’s nearby. Apart from the highly detailed neural radiance field, F3RM also builds a feature field to augment geometry with semantic information. You see, it uses CLIP, a vision foundation model trained on hundreds of millions of images to efficiently learn visual concepts. Hence, with the pictures’ 2 features reconstructed in an enhanced form, F3RM effectively lifts the 2D features into a 3D representation.

“Visual perception was defined by David Marr as the problem of knowing ‘what is where by looking,'” said Phillip Isola, senior author on the study, MIT associate professor of electrical engineering and computer science, and CSAIL principal investigator. “Recent foundation models have gotten really good at knowing what they are looking at; they can recognize thousands of object categories and provide detailed text descriptions of images. At the same time, radiance fields have gotten really good at representing where stuff is in a scene. The combination of these two approaches can create a representation of what is where in 3D.”

Having lifted the 2D features into a 3D representation, it’s time for us to understand how the new system helps in actually controlling the objects. Basically, after receiving a few demonstrations, the robot applies what it knows about geometry and semantics to grasp objects it has never encountered before. As a result, when a user submits a text query, the robot searches through the space of possible grasps to identify those most likely to succeed in picking up the object requested by user. During this selection process, each potential option is scored based on its relevance to the prompt, similarity to the demonstrations the robot has been trained on, and if it causes any collisions. Based on those scores, the robot picks the most suitable course of action. In case that somehow wasn’t impressive enough, then it must be mentioned how F3RM further enables users to specify which object they want the robot to handle at different levels of linguistic detail. To explain it better, if there is a metal mug and a glass mug, the user can ask the robot for the “glass mug.” Hold on, we aren’t done, as even if both the mugs are of glass and and one of them is filled with coffee and the other with juice, the user can ask the robot for “glass mug with coffee.”

“If I showed a person how to pick up a mug by the lip, they could easily transfer that knowledge to pick up objects with similar geometries such as bowls, measuring beakers, or even rolls of tape. For robots, achieving this level of adaptability has been quite challenging,” said William Shen, Ph.D. student at MIT, and co-lead author on the study.”F3RM combines geometric understanding with semantics from foundation models trained on internet-scale data to enable this level of aggressive generalization from just a small number of demonstrations.”

In a test conducted on the system’s ability to interpret open-ended requests from humans, the researchers prompted the robot to pick up Baymax, a character from Disney’s “Big Hero 6.” Interestingly, F3RM had never been directly trained to pick up a toy of the cartoon superhero, but the robot was successful in leveraging its spatial awareness and vision-language features from the foundation models to decide which object to grasp and how to pick it up.

“Making robots that can actually generalize in the real world is incredibly hard,” said Ge Yang, postdoc at the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions. “We really want to figure out how to do that, so with this project, we try to push for an aggressive level of generalization, from just three or four objects to anything we find in MIT’s Stata Center. We wanted to learn how to make robots as flexible as ourselves, since we can grasp and place objects even though we’ve never seen them before.”

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The researching teams at Massachusetts Institute of Technology and NVIDIA have successfully developed two techniques that are meant to accelerate the processing of sparse tensors, a type of data structure that’s used for high-performance computing tasks. According to certain reports, the techniques in question will bring significant improvements to the performance and energy-efficiency of systems like the massive machine-learning models that drive generative artificial intelligence. Talk about how they will do so, the answer is rooted in exploiting sparsity, zero values in the tensors. You see, given these values have no meaningful role whatsoever; one can just skip over them and save on both computation and memory. This way it becomes possible to compress the tensor and allow larger portion to be stored in on-chip memory. That being said, there is also a reason why it hasn’t been achieved so far. For starters, finding the non-zero values in a large tensor is no easy task, as existing approaches often limit the locations of non-zero values by enforcing a sparsity pattern to simplify the search. With a pre-defined pattern now in place, the variety of tensors that can be processed efficiently becomes too thin. Another challenge in play here is how the number of non-zero values can actually vary in different regions of the tensors. Such variance makes it difficult to determine how much space is required to store different regions in memory. To overcome the problem in question, more space than necessary is often allocated, which in turn, forces the storage buffer to be underutilized. Extending the ripple effect is a notable increase in off-chip memory traffic that, of course, requires extra computation. But how did MIT and NVIDIA researchers solve this conundrum? Out of their two techniques, they got the first one to efficiently find the non-zero values for a wider variety of sparsity patterns. In second solution’s case, they created a method that can handle the case where the data doesn’t fit in memory. The stated focus is intended to go a long way when it comes to increasing the utilization of the storage buffer, while simultaneously reducing off-chip memory traffic. Although a tad different in their function, both methods boost the performance and reduce the energy demands of hardware accelerators.

“Typically, when you use more specialized or domain-specific hardware accelerators, you lose the flexibility that you would get from a more general-purpose processor, like a CPU. What stands out with these two works is that we show that you can still maintain flexibility and adaptability while being specialized and efficient,” said Vivienne Sze, associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory of Electronics (RLE), and co-senior author of papers on both advances.

In reference to hardware accelerators, the researching teams have also developed a dedicated and improved iteration of the same. Named HighLight, the accelerator can handle a wide variety of sparsity patterns and still perform well when running models that don’t have any zero values. It delivers on this promised value proposition through “hierarchical structured sparsity”, which represents a wide variety of sparsity patterns that are actually just made from multiple simple sparsity patterns. Here, they divide the values in a tensor into smaller blocks, where each block has its own simple, sparsity pattern (perhaps two zeros and two non-zeros in a block with four values). Next up, they combine the blocks into a hierarchy. One can continue to combine blocks into larger levels, but the patterns remain simple at each step. This sort of capability enables us to find and skip zeros, meaning it can also effectively root out the problem of excess computation. If we are strictly talking numbers, then we must mention that the accelerator design is said to be six times more energy-efficient than other approaches.

“In the end, the HighLight accelerator is able to efficiently accelerate dense models because it does not introduce a lot of overhead, and at the same time it is able to exploit workloads with different amounts of zero values based on hierarchical structured sparsity,” said Yannan Nellie Wu, co-lead author on the study.

For the future, the researching teams at MIT and NVIDIA plan to apply hierarchical structured sparsity to more types of machine-learning models and different types of tensors in those models.

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Google has officially announced the launch of various new AI features that are geared towards helping Google Maps provide more immersive navigation, easier-to-follow driving directions, and better organized search results. For starters, the tech behemoth has updated the application’s search function to make it easier to find specific things near you. In practice, when you search for something, you will find specific photo results of what you are looking for. These photos come after AI and advanced image recognition models link-up to analyze the picture content shared by other users on Google Maps. Next up, the navigation platform will help you figure out whether the EV charging station you are planning to sop by is actually working or not. You see, according to studies, nearly 25 percent of chargers are down or inoperable at any given time. Hence, Google Maps will now be able to inform you on when a charger was last used, considering if the station was used a few hours ago, chances are it’s working. On the other hand, if it’s been a few days or weeks since it was last used, the indication might be to find another charger. Complimenting the same are more EV details such as charger’s compatibility with your EV and whether it’s fast, medium, or slow. Hold on, there is more for all EV owners and car companies. You see, Google has also made a call to offer updated Places APIs to help build out better features for cars with navigation systems based on Google Maps. As a result, car companies can use the Places API to build out more EV charging information so their customers can see real-time location information, plug type, and charging speeds directly on their vehicle’s infotainment screens. Talk about improving the driver’s experience, the search engine giant further solidifies that pledge through Immersive View, which was first announced earlier this year. Immersive View, for anyone who doesn’t know, brings a 3D view of a place to help users see where they’re supposed to go, while also offering other tidbits of information, like local business locations, weather, and traffic.

“AI has really supercharged the way we map,” said Chris Phillips, vice president and general manager of Geo, the team at Google that includes all of its geospatial location mapping products. “It plays a key role in everything from helping you navigate, [helping you] commute, discover new restaurants, where to go, when to go. These are all really important decisions that people are making all the time.”

Another detail worth a mention would be how Google is rebranding its augmented reality feature “Search with Live View” to “Lens in Maps”. Basically, you can use this feature by tapping on the Lens in the search bar. Then, you must hold up your camera to find information about the nearest train stations, coffee shops, ATMs, or whatever happens to be in close proximity of your location. Coming back to in-car capabilities, Google is set to bring HOV lanes for US drivers, a feature workable on both Android and iOS devices, as well as in cars with Google built-in. Alongside HOV lanes, you can also expect a lowdown on the  speed limit information. Rounding up these highlights is the new look in which the whole value proposition will be presented. By new look, we mean updated colors, more realistic buildings, and improved lane details for tricky highway exits. The stated makeover will be accessible across 12 countries, including the US, Canada, France, and Germany.

“The foundation of all the work we do is to build the most comprehensive, fresh, accurate information to represent the real world,” Phillips said. “This is key for us, and we like to talk about the map as being alive.”

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VERSE, a Scottsdale, Arizona-based company, has officially announced the launched of its highly-anticipated metaverse, a groundbreaking and interconnected virtual world. According to certain reports, the company’s brand-new world sets itself apart right from the get-go with a single-server approach, considering literally all metaverse iterations we have seen so far mandate the use of multiple servers for a smooth experience. An even more impressive feature is how, despite having just one server, VERSE’s latest brainchild is able to offer over 225 sq km of interconnected virtual space at launch. Within the said space, the metaverse brings to the fore more than 20,000 land parcels that a user can buy, personalize, and even resell or rent. Moving on to the technology’s virtual capabilities, every feature of THE VERSE is accessible in the virtual reality realm. Such an aspect eliminates the need for a user to indulge in any offline interactions. As a result, you can expect from this virtual world an almost unparalleled level of immersion.

When quizzed regarding the new virtual world, Blake Spurgin, Co-founder of THE VERSE, responded by saying:

“In creating THE VERSE, we envisioned a digital realm that echoes the endless possibilities of our universe. We’re not just offering another game; we’re pioneering a truly interconnected metaverse where every user can create, connect, and thrive. This is just the beginning and we’re excited to lead this virtual revolution,”

In order to enhance the user experience, VERSE has also put on the offer a diverse set of engaging activities. Talk about these activities, they will include jetpacking, driving, boating, intricate quests, shooting, exploring, and a whole lot more. Not just that, the platform also has an intriguing and dedicated Underworld, which boasts various unique buildings and aesthetics in its own right. Now, given the extensive technological prowess in play, it becomes really important to present every detail in a complimentary manner. This is exactly why, rather than opting for that highly-prevalent simple brand of graphics, THE VERSE has pledged its allegiance to an intuitive and realistic route, thus ensuring virtual world’s framework doesn’t go outdated over time.

As for the availability, you can buy the VERSE from App Lab’s platform for $29.99, and once you do so, you can begin to take on its perks through Meta Quest 2 and Quest 3.

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The researching team at Massachusetts Institute of Technology has successfully concluded a study where they tested a method which is usually put into practice for verifying an AI’s system efficiency. To give you some context, the method in focus here, named formal specifications, uses mathematical formulas that can be translated into natural-language expressions. The purpose of doing so is essentially to spell out decisions an AI will make in a way that is interpretable to humans. However, if we put our stock in the latest study, we’ll see how the method is not as interpretable as it has been advertised since a long time now. Before reaching on such a sensational claim, the researchers conducted an experiment where they enlisted both a contingent of experts in formal specifications method and a group of non-experts. These people were provided formal specifications in three ways i.e. a “raw” logical formula, the formula translated into words closer to natural language, and a decision-tree format. Once that bit was done, they were asked to validate a fairly simple set of behaviors with a robot playing a game of capture the flag. Their practical task was just to answer the question “If the robot follows these rules exactly, does it always win?” Going by the available details, the validation performance could only manage about 45% worth of accuracy.

“When researchers say ‘our machine learning system is accurate,’ we ask ‘how accurate?” and ‘using what data?’ and if that information isn’t provided, we reject the claim. We haven’t been doing that much when researchers say ‘our machine learning system is interpretable,’ and we need to start holding those claims up to more scrutiny,” said Hosea Siu, a researcher in the MIT Lincoln laboratory’s AI Technology Group.

In order to understand the significance of such a development, we must acknowledge that interpretability is important because it allows humans to place their trust in a machine when used across a real and practical backdrop. However, given the machine learning process happens in somewhat of a “black box”, it has remained a massive challenge to gain meaningful clarity on the subject. Coming back to the study, interestingly enough, while experts on formal specifications did better than the rookies, the former group was found to ignore the rule sets allowing for game losses. Instead, they placed an overt amount of trust in the correctness of specifications presented to them, sparking a major confirmation bias concern.

“We don’t think that this result means we should abandon formal specifications as a way to explain system behaviors to people. But we do think that a lot more work needs to go into the design of how they are presented to people and into the workflow in which people use them,” said Siu.

The study, although pretty significant in its own right, is only one part of a larger project, which is designed to improve the relationship between robots and human operators. You see, programming robots in the way it’s done right often ends up diminishing the operators’ influence on the technology. Hence, the stated MIT project will try and let these operators teach tasks to robots directly, similar to how a human being would be trained. This won’t just bolster the individual’s trust in the robot, but it also help the robot become more adaptable.

“Our results push for the need to do human evaluations of certain systems and concepts of autonomy and AI before too many claims are made about their utility with humans,” said Siu.

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